How Do Computational Models of the Role of Dopamine as a Reward Prediction (original) (raw)
2008
A review of the current dopamine theories of schizophrenia reveals a likely imbalance between cortical and subcortical microcircuits due to an insufficient inhibitory brake, leading to a disruption of the dopamine system and the classic positive psychotic symptoms, negative symptoms and cognitive deficits associated with the disorder. Recent computational models have modelled the role of dopamine as a reward prediction error, using Temporal Difference and have successfully shown how these symptoms could arise from a disturbance to the dopamine system. We review these models in the light of dopamine theories of schizophrenia and highlight some of the major points that should be addressed by future computational models.
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2006
How Do Computational Models of the Role of Dopamine as a Reward Prediction Error Map on to Current Dopamine Theories of Schizophrenia? Angela J. Thurnham* (a.j.thurnham@herts.ac.uk), D. John Done** (d.j.done@herts.ac.uk), Neil Davey* (n.davey@herts.ac.uk), Ray J. Frank* (r.j.frank@herts.ac.uk) School of Computer Science,* School of Psychology, **University of Hertfordshire, College Lane, Hatfield, Hertfordshire. AL10 9AB United Kingdom reward prediction, or TD error, including evidence that the RPE model of dopamine activity applies to humans as well as primates. The biological plausibility of existing neural network models by Cohen & Servan-Schreiber, (1992); Braver Barch & Cohen, (1999); Suri & Schultz, (1999); Rougier, Noelle, Braver, Cohen & O’Reilly, (2005) and O’Reilly & Frank, (2006) are then discussed in the light of the afore-mentioned dopamine theories of schizophrenia. Finally, we conclude with four major questions arising from recent dopamine theories of schizophrenia th...
Frontiers in psychiatry, 2013
Abnormalities in reinforcement learning are a key finding in schizophrenia and have been proposed to be linked to elevated levels of dopamine neurotransmission. Behavioral deficits in reinforcement learning and their neural correlates may contribute to the formation of clinical characteristics of schizophrenia. The ability to form predictions about future outcomes is fundamental for environmental interactions and depends on neuronal teaching signals, like reward prediction errors. While aberrant prediction errors, that encode non-salient events as surprising, have been proposed to contribute to the formation of positive symptoms, a failure to build neural representations of decision values may result in negative symptoms. Here, we review behavioral and neuroimaging research in schizophrenia and focus on studies that implemented reinforcement learning models. In addition, we discuss studies that combined reinforcement learning with measures of dopamine. Thereby, we suggest how reinfo...
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